{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/experimental/semantic-search-intro/sbert.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/pinecone-io/examples/blob/master/learn/experimental/semantic-search-intro/sbert.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BERT\n",
    "\n",
    "In this notebook we'll take a look at how we can use transformer models (like BERT) to create sentence vectors for calculating similarity. Let's start by defining a few example sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"purple is the best city in the forest\"\n",
    "b = \"there is an art to getting your way and throwing bananas on to the street is not it\"  # this is very similar to 'g'\n",
    "c = \"it is not often you find soggy bananas on the street\"\n",
    "d = \"green should have smelled more tranquil but somehow it just tasted rotten\"\n",
    "e = \"joyce enjoyed eating pancakes with ketchup\"\n",
    "f = \"as the asteroid hurtled toward earth becky was upset her dentist appointment had been canceled\"\n",
    "g = \"to get your way you must not bombard the road with yellow fruit\"  # this is very similar to 'b'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Installing dependencies needed for this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -qU transformers sentence-transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModel\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize our HF transformer model and tokenizer - using a pretrained SBERT model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/bert-base-nli-mean-tokens')\n",
    "model = AutoModel.from_pretrained('sentence-transformers/bert-base-nli-mean-tokens')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tokenize all of our sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens = tokenizer([a, b, c, d, e, f, g],\n",
    "                   max_length=128,\n",
    "                   truncation=True,\n",
    "                   padding='max_length',\n",
    "                   return_tensors='pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['input_ids', 'token_type_ids', 'attention_mask'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 101, 6379, 2003, 1996, 2190, 2103, 1999, 1996, 3224,  102,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens['input_ids'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Process our tokenized tensors through the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_keys(['last_hidden_state', 'pooler_output'])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs = model(**tokens)\n",
    "outputs.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see the final embedding layer, *last_hidden_state*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.6239, -0.2058,  0.0411,  ...,  0.1490,  0.5681,  0.2381],\n",
       "        [-0.3694, -0.1485,  0.3780,  ...,  0.4204,  0.5553,  0.1441],\n",
       "        [-0.7221, -0.3813,  0.2031,  ...,  0.0761,  0.5162,  0.2813],\n",
       "        ...,\n",
       "        [-0.1894, -0.3711,  0.3034,  ...,  0.1536,  0.3265,  0.1376],\n",
       "        [-0.2496, -0.5227,  0.2341,  ...,  0.3419,  0.3164,  0.0256],\n",
       "        [-0.3311, -0.4430,  0.3492,  ...,  0.3655,  0.2910,  0.0728]],\n",
       "       grad_fn=<SelectBackward>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings = outputs.last_hidden_state\n",
    "embeddings[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([128, 768])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we have our vectors of length *768*, but we see that these are not *sentence vectors* because we have a vector representation for each token in our sequence (128 in total). We need to perform a mean pooling operation to create the sentence vector.\n",
    "\n",
    "The first thing we do is multiply each value in our `embeddings` tensor by its respective `attention_mask` value. The `attention_mask` contains **1s** where we have 'real tokens' (eg not padding tokens), and 0s elsewhere - so this operation allows us to ignore non-real tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([7, 128, 768])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = tokens['attention_mask'].unsqueeze(-1).expand(embeddings.size()).float()\n",
    "mask.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 1., 1.,  ..., 1., 1., 1.],\n",
       "        [1., 1., 1.,  ..., 1., 1., 1.],\n",
       "        [1., 1., 1.,  ..., 1., 1., 1.],\n",
       "        ...,\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a masking array that has an equal shape to our output `embeddings` - we multiply those together to apply the masking operation on our outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.6239, -0.2058,  0.0411,  ...,  0.1490,  0.5681,  0.2381],\n",
       "        [-0.3694, -0.1485,  0.3780,  ...,  0.4204,  0.5553,  0.1441],\n",
       "        [-0.7221, -0.3813,  0.2031,  ...,  0.0761,  0.5162,  0.2813],\n",
       "        ...,\n",
       "        [-0.0000, -0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
       "        [-0.0000, -0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
       "        [-0.0000, -0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000]],\n",
       "       grad_fn=<SelectBackward>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masked_embeddings = embeddings * mask\n",
    "masked_embeddings[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sum the remaining embeddings along axis 1 to get a total value in each of our 768 values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([7, 768])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summed = torch.sum(masked_embeddings, 1)\n",
    "summed.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we count the number of values that should be given attention in each position of the tensor (+1 for real tokens, +0 for non-real)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([7, 768])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counted = torch.clamp(mask.sum(1), min=1e-9)\n",
    "counted.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we get our mean-pooled values as the `summed` embeddings divided by the number of values that should be given attention, `counted`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([7, 768])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_pooled = summed / counted\n",
    "mean_pooled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have our sentence vectors, we can calculate the cosine similarity between each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to numpy array from torch tensor\n",
    "mean_pooled = mean_pooled.detach().numpy()\n",
    "\n",
    "# calculate similarities (will store in array)\n",
    "scores = np.zeros((mean_pooled.shape[0], mean_pooled.shape[0]))\n",
    "for i in range(mean_pooled.shape[0]):\n",
    "    scores[i, :] = cosine_similarity(\n",
    "        [mean_pooled[i]],\n",
    "        mean_pooled\n",
    "    )[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000012,  0.1869276 ,  0.2829769 ,  0.29628235,  0.2745102 ,\n",
       "         0.10176259,  0.21696258],\n",
       "       [ 0.1869276 ,  1.        ,  0.72058779,  0.51428956,  0.1174964 ,\n",
       "         0.19306925,  0.66182363],\n",
       "       [ 0.2829769 ,  0.72058779,  1.00000024,  0.4886443 ,  0.23568943,\n",
       "         0.17157131,  0.55993092],\n",
       "       [ 0.29628235,  0.51428956,  0.4886443 ,  0.99999988,  0.26985496,\n",
       "         0.37889433,  0.52388811],\n",
       "       [ 0.2745102 ,  0.1174964 ,  0.23568943,  0.26985496,  0.99999988,\n",
       "         0.23422126, -0.01599787],\n",
       "       [ 0.10176259,  0.19306925,  0.17157131,  0.37889433,  0.23422126,\n",
       "         1.00000012,  0.22319663],\n",
       "       [ 0.21696258,  0.66182363,  0.55993092,  0.52388811, -0.01599787,\n",
       "         0.22319663,  1.        ]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize these scores using `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,9))\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g']\n",
    "sns.heatmap(scores, xticklabels=labels, yticklabels=labels, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Using sentence-transformers\n",
    "\n",
    "The `sentence-transformers` library allows us to compress all of the above into just a few lines of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "model = SentenceTransformer('bert-base-nli-mean-tokens')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We encode the sentences (producing our mean-pooled sentence embeddings) like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence_embeddings = model.encode([a, b, c, d, e, f, g])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And calculate the cosine similarity just like before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import numpy as np\n",
    "\n",
    "# calculate similarities (will store in array)\n",
    "scores = np.zeros((sentence_embeddings.shape[0], sentence_embeddings.shape[0]))\n",
    "for i in range(sentence_embeddings.shape[0]):\n",
    "    scores[i, :] = cosine_similarity(\n",
    "        [sentence_embeddings[i]],\n",
    "        sentence_embeddings\n",
    "    )[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.18692753,  0.28297687,  0.29628229,  0.27451015,\n",
       "         0.1017626 ,  0.21696255],\n",
       "       [ 0.18692753,  0.99999988,  0.72058773,  0.5142895 ,  0.11749639,\n",
       "         0.19306931,  0.66182363],\n",
       "       [ 0.28297687,  0.72058773,  1.00000024,  0.48864418,  0.2356894 ,\n",
       "         0.17157122,  0.55993092],\n",
       "       [ 0.29628229,  0.5142895 ,  0.48864418,  0.99999976,  0.26985493,\n",
       "         0.3788943 ,  0.52388811],\n",
       "       [ 0.27451015,  0.11749639,  0.2356894 ,  0.26985493,  0.99999982,\n",
       "         0.23422126, -0.01599786],\n",
       "       [ 0.1017626 ,  0.19306931,  0.17157122,  0.3788943 ,  0.23422126,\n",
       "         1.00000012,  0.22319666],\n",
       "       [ 0.21696255,  0.66182363,  0.55993092,  0.52388811, -0.01599786,\n",
       "         0.22319666,  1.        ]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  }
 ],
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